Mastering Web Scraping with Crawl for AI: Three Powerful Strategies to Extract Knowledge for LLMs

In the rapidly evolving landscape of AI and large language models, having access to up-to-date information is crucial. Web scraping has become an essential technique for feeding relevant data into AI systems, and there’s one tool that stands out from the rest: Crawl for AI.

With over 42,000 stars on GitHub, this open-source tool has proven itself as the industry standard for extracting web content in an AI-ready format. Let’s explore three powerful strategies that enable you to extract knowledge from virtually any website.

Why Choose Crawl for AI?

Before diving into the strategies, it’s important to understand what makes Crawl for AI exceptional:

• It’s blazingly fast, processing websites in seconds
• Produces markdown output – the optimal format for LLMs
• Intelligently extracts content while removing irrelevant elements
• Open-source and constantly improving

Many AI-powered services, including leading coding assistants, likely use Crawl for AI or similar technology to keep their knowledge bases current with the latest documentation.

Getting Started

Setting up Crawl for AI is straightforward. You need Python installed, then simply run:

pip install crawl-for-ai

After installation, run the setup command to install the playwright browser, which enables terminal-based web scraping.

Strategy 1: Crawling via Sitemap

The most efficient way to scrape a website is through its sitemap. Many sites provide this XML file that lists all available URLs.

To implement this strategy:

1. Locate the sitemap (usually at domain.com/sitemap.xml)
2. Extract all URLs from the sitemap
3. Use Crawl for AI’s parallel processing to fetch content in batches

The code implementation is straightforward – create an async web crawler instance, then use the a_run_many function to process batches of URLs simultaneously.

This method is ideal when sitemaps are available as it guarantees complete coverage of the website’s content.

Strategy 2: Recursive Crawling

When a sitemap isn’t available, Crawl for AI can dynamically discover pages through navigation.

Here’s how it works:

1. Start with a homepage or entry point
2. Extract internal links from the page
3. Recursively visit these links up to a specified depth
4. Collect markdown from all visited pages

The magic happens through the internal_links property, which identifies links within the same domain. By processing these links recursively, you can build a comprehensive knowledge base without a predefined sitemap.

While not guaranteed to find every page (as sitemaps would), this method offers flexibility for sites without well-defined structure.

Strategy 3: LLMs.text Files

Many documentation sites now offer a special format specifically designed for large language models, typically found at:

• /llms.text
• /llms-full.text

These files contain all documentation in a single page, pre-formatted for AI consumption. This approach simplifies the scraping process significantly – just download a single file and chunk it appropriately.

The implementation for this strategy involves:

1. Fetch the LLMs.text file
2. Split the document based on headers and logical sections
3. Create chunks that maintain contextual information

This is often the most efficient approach for framework documentation when available.

Smart Chunking for Better Comprehension

Once you’ve extracted content using any of these strategies, proper chunking becomes essential. Markdown’s structure with headers and subheadings provides natural break points for creating meaningful chunks.

A smart chunking strategy might:

• Split documents at major headings
• Keep related subsections together
• Maintain contextual information within each chunk
• Avoid splitting in the middle of code examples or tables

This ensures that when your AI retrieves information, it gets complete and coherent sections rather than fragmentary content.

Putting It All Together

The ideal implementation intelligently determines which strategy to use based on the URL pattern:

1. Check if an LLMs.text file is available
2. If not, look for a sitemap.xml
3. As a last resort, use recursive crawling

By combining these approaches, you can extract knowledge from virtually any website and transform it into a format ideal for vector databases and retrieval-augmented generation (RAG).

Conclusion

Crawl for AI represents a significant advancement in our ability to feed current, relevant information into AI systems. Whether you’re building a specialized agent, enhancing a coding assistant, or creating a knowledge base for unique domains, these three strategies provide a comprehensive approach to web scraping.

By leveraging sitemaps, recursive crawling, or LLMs.text files, you can quickly build robust AI systems with up-to-date information from across the web, enabling more accurate and helpful AI assistance.